Estimating Variogram Uncertainty

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Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories (2009, background): “Common problems of i) de-trending residuals, ii) heterogenous variance, and iii) estimating the ‘smoothness parameter’ ν in Matérn's variogram model were duly recognized (see also Glatzer & Muller, 2004; Marchant & Lark, 2004; Zhao & Wall, 2004).”
Non-stationary Geostatistical Modeling Based on Distance Weighted Statistics and Distributions (2012, background): “These fluctuations are related to the error that arises from differences between the estimated statistics and the statistics of the unique spatial distribution of a geological attribute (Marchant and Lark 2004).”
Confronting uncertainty in model-based geostatistics using Markov Chain Monte Carlo simulation (2011, background): “The variance of the prediction error, and the Fisher information (sensitivity) matrix contain the desired information about the scale and orientation of the parameter uncertainty (Marchant and Lark, 2004; Minasny and McBratney, 2005).”